High Quality Simulated Video from Static ImagesUC San Diego - Team Internship Program

Alexander Chan | Nima Hashemi

Project Supervisor - Dr. Shay Har-Noy Technical Lead - David Schmidt

Stitched Mosaic(Made from1600x1200

jpeg Images)

EnerView Real-Time

Video

(1)

(2)

(3) 1. User requests region of interest on Video Feed

2. Find corresponding area on mosaic by matching video and mosaic timestamp

3. High Quality Image Viewer scrolls along mosaic in synchronization with video

4. Suspicious object spotted behind the tree - an airplane!

5. Grayed area of mosaic demonstrates regions that will be stitched dynamically

(5)

(4)

1. UAV captures video / images of ground dynamically2. Data transmitted via airborne modem3. Data passes through 10 mb/s data link4. Video/Images received through ground modem

(2)

(3)

(4)

(1)

Screenshots:

Extract and match image features using SURF algorithm and NCC matching.

Use RANSAC to determine inliers (accurate feature matches) and find a homography relating the two images.

Apply homography and stitch images together by position mapping and blurring.

High Quality Image Viewer

Window

1. OpenCV (C++) Image Processing Library used extensively to implement image stitching algorithm

2. IJG Library and existing EnerView System used to design High Quality Image Viewer Window

• UAV must travel a straight path with no rotation, making the images it is taking appear linear. • A more robust stitching algorithm is needed to allow for

arbitrary UAV movement.

• Timestamp correlation between video and still images may have a 3-5 second delay, causing discrepancies between EnerView Video feed and High Quality Image Viewer. • Images must be captured by camera supporting EXIF format.

• The EnerView System features time and CPU consuming processes, particularly the image stitching algorithm.• Multithreading between user interface and stitching algorithm.• Implement fixed upper bounds on the number of SURF

features extracted and number of RANSAC iterations.• Further image down-sampling before feature point extraction.

The current implementation demonstrates that it is advantageous to use simulated video from still images to identify suspect features. This is particularly true when users are interested in zooming in on specific features. Zooming using our approach is achievable to a very high resolution, especially as compared to video zooming.

Direction of video motion